What Are In-House Data Science Platforms?

Data science platforms have been a major topic in numerous industries since the past decade. Telecommunications, retail, healthcare, manufacturing, and government, almost every major sector has been taken over by these platforms. Leading experts even suggest that by 2026, its market value will skyrocket to $165.5 billion.

But while it’s not new to data science experts, the majority of people, including you, might not know what a data science platform does or how it works. But now there’s another new trend in this sector–in-house data science platforms.

At this point, your questions must be piling up one after the other, but rest assured, this guide will answer your questions and more regarding in-house data science platforms. 

What Is A Data Science Platform?

At its core, a data science platform is a digital workstation for data scientists. It’s where the majority of data science tasks are done. It’s where they find the necessary tools, too. 

Basically, it’s a platform that helps data science experts throughout a data science project’s life cycle, including:

  • Data Collection
  • Data Preparation
  • Training
  • Development
  • Parameter Tuning
  • Deployment

Data science platforms are popular for several reasons. Smoother communication, easier collaboration, and web accessibility are just a few examples. Eventually, this technology reached the commerce sector, which is probably where your company belongs to, and this then led to the term ‘in-house data science platforms.’

What Makes In-House Data Science Platforms Different?

When a company wants a dedicated data science platform, they usually hire a third-party agency to develop the platform for them. In other words, they outsource the data science platform.

Conversely, if your company wants to create a data science platform without any agency’s help, that’s what we call in-house data science platforms. Simply put, it’s when a company develops the platform independently by using their own employees and resources.

However, creating a data science platform can be challenging and requires a considerable investment. As such, a company must first consider a few things. Here’s an official source to help you in that aspect. 

Regardless, business owners often have second thoughts about developing an in-house data science platform due to its difficulty, so is it really worth it?

Should You Develop Data Science Platforms In-House? 

If your company doesn’t have as many data experts as other agencies, it would seem like developing a data science platform is a bad choice. While that’s partially true, there are cases where ‘insourcing’ data science tasks offers more benefits than when you outsource them. 

Here are some examples: 

  • Developing a data science platform in-house provides greater control over your data. 
  • It’s easier to add functions and tools into the platform than when you outsource the development to a third-party agency. 
  • Outsourcing leads to the risk of exposing confidential information to outsiders.
  • Misunderstanding is common when collaborating with a third-party agency.

Unfortunately, struggles are inevitable, even if you outsource the platform. You’ll have to organize a team of data science experts, purchase data science tools, and more. Naturally, you’d want to know if it’s worthwhile to develop a data science platform. 

Why Do You Need A Data Science Platform?

If your company has been around for a long time, there must already be a few teams within the organization. Sales, customer support, and human resources (HR) are some examples. While you may not be aware of it, each team uses different platforms. 

For example, the sales team uses Customer Relationship Management (CRM) systems. The customer support team uses a ticketing platform, and the HR team uses team management software. Either way, each team will often require a platform to aid with their operations; the data science team is no exception. And yes, what they need is a data science platform.

But you have to remember that not all in-house data science platforms are useful. Put simply, the concept of success and failure also applies to the development of data science platforms. So, how will you know if the platform is a success?

What Makes A Data Science Platform Effective?

Data science platforms are, without a doubt, powerful tools that aid in data science tasks. So, if you feel like there’s no change within your company’s data science operations, there must be something wrong with the platform, which begs the question, ‘what makes an in-house data science platform effective?’ 

Here are a few things a data science platform must do: 

  • Allows collaboration between other teams within the company. 
  • Makes complex data science tasks possible without the help of DevOps.
  • Produces data science insights and conveys them to the company’s stakeholders. 
  • Enables the use of open-source tools. 


While in-house data science platforms certainly have tremendous benefits to a company, it’s not always a good idea to develop the platform right off the bat, especially if your company is currently busy with some other matters. Perhaps you’re busy with product development or maybe you’re currently working on cybersecurity. Regardless, it’s essential to know when’s the right time to develop an in-house data science platform.

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